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LeonChaoX
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scvelo

Infer RNA velocity and latent time from spliced and unspliced mRNA in AnnData.

783|65|Updated Feb 27, 2026
Search Tags:#rna velocity#single-cell rna-seq#trajectory inference#annadata#latent time#driver genes
One-click install
npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill scvelo-leonchaox
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvelo
Source: https://github.com/LeonChaoX/qinyan-academic-skills/tree/main/skills/05-%E7%94%9F%E7%89%A9%E4%BF%A1%E6%81%AF%E4%B8%8E%E5%9F%BA%E5%9B%A0%E7%BB%84%E5%AD%A6/scvelo
Command: npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill scvelo-leonchaox

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scvelo, scanpy, numpy, matplotlib, os, and includes scripts (resource) and references (resource) components.

What problem does it solve?

scVelo helps you infer directional cell state transitions from single-cell RNA-seq snapshot data without needing time-course experiments, using unspliced and spliced mRNA dynamics.

Core Features & Use Cases

  • RNA velocity estimation: Compute velocity fields and transition likelihoods using stochastic or dynamical kinetic models.
  • Trajectory direction & pseudotime: Recover latent time (dynamical model) and velocity-based pseudotime to order cells along differentiation.
  • Driver gene identification: Rank and visualize genes whose splicing kinetics best explain the inferred dynamics.
  • Visualization & model diagnostics: Generate velocity arrows/streams, confidence/coherence metrics, and (optionally) PAGA graphs informed by velocity.

Quick Start

Use scvelo on your AnnData object that contains layers for spliced and unspliced counts to estimate velocities, compute latent time, and rank driver genes in a single run.

Frequently Asked Questions about scvelo

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I infer RNA velocity from single-cell RNA-seq snapshot data?▼

RNA velocity infers directional cell state transitions from single-cell RNA-seq snapshot data by analyzing spliced and unspliced mRNA dynamics to model cellular differentiation without requiring time-course experiments.

What is needed to compute latent time and trajectory direction using scVelo?▼

Computing latent time and trajectory direction requires an AnnData input object containing unspliced and spliced count layers, neighborhood and moment computation, and execution of scVelo dynamical model steps.

Can I use Scanpy manifold clustering results with RNA velocity estimation?▼

Yes, RNA velocity estimation complements Scanpy-based manifold clustering by adding directional information to the existing neighborhood graph, enabling velocity arrow visualization and transition likelihood computation.

How do I identify driver genes from spliced and unspliced mRNA kinetics?▼

Driver genes are identified by ranking genes whose splicing kinetics best explain the inferred RNA velocity dynamics, using stochastic or dynamical kinetic models applied to the velocity graph.

What single-cell RNA-seq formats are supported for velocity graph construction?▼

Velocity graph construction supports the AnnData format, which must contain dedicated layers for spliced and unspliced mRNA counts alongside the neighborhood graph computed from the single-cell RNA-seq data.

Why does trajectory inference fail without unspliced and spliced layers in my AnnData object?▼

Trajectory inference fails because RNA velocity estimation fundamentally depends on the ratio of unspliced to spliced mRNA layers within the AnnData object to calculate kinetic rates and project future cell states.

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